---
tags:
- generated_from_trainer
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-tweets
  results: []
datasets:
- jhonparra18/petro-tweets
language:
- es
metrics:
- perplexity
license: apache-2.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-spanish-wwm-cased-finetuned-tweets

This model is a fine-tuned version of bert-base-spanish-wwm-cased on the dataset petro-tweets.
It achieves the following results on the evaluation set:
- Loss: 2.7191
- Perplexity: 15.17

## Model description

This model is a fine-tuned version of bert-base-spanish-wwm-cased using the dataset from petro-tweets (available in Huggin Face).
The purpose is to extend the bert-base-spanish-cased domain, which, once fine-tuned, will be modified for the fill-in-the-gaps task.

## Intended uses & limitations

The use is limited to school use and the limitations have to do with the size of the dataset, since it does not allow for a large contribution, a larger dataset would have to be used to get a larger contribution.


## Training and evaluation data

I did a training that gives the training and validation set loss. (It takes a lot of time. If you're using colab, I recommend to use less Epochs because the result does not change too much, and even though the loss is quite high, the performance of the model based on the perplexity is quite good)
Also, I checked the perplexity, which is one good measure for Languages Models. As seen before, it gives a good perplexity, because it's quite low.

- Evaluation: I checked the performance of my model in the notebook provided, just by generating examples. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9867        | 1.0   | 79   | 2.8373          |
| 2.1251        | 2.0   | 158  | 2.8581          |
| 2.0998        | 3.0   | 237  | 2.7878          |
| 2.1207        | 4.0   | 316  | 2.8046          |
| 2.1268        | 5.0   | 395  | 2.7893          |
| 2.1453        | 6.0   | 474  | 2.8231          |
| 2.1548        | 7.0   | 553  | 2.7538          |
| 2.1582        | 8.0   | 632  | 2.7992          |
| 2.1775        | 9.0   | 711  | 2.6910          |
| 2.1962        | 10.0  | 790  | 2.7582          |
| 2.226         | 11.0  | 869  | 2.7252          |
| 2.2153        | 12.0  | 948  | 2.7437          |
| 2.2421        | 13.0  | 1027 | 2.7776          |
| 2.2297        | 14.0  | 1106 | 2.7257          |
| 2.2265        | 15.0  | 1185 | 2.7722          |
| 2.2187        | 16.0  | 1264 | 2.7475          |
| 2.2272        | 17.0  | 1343 | 2.7317          |
| 2.2315        | 18.0  | 1422 | 2.6876          |
| 2.2619        | 19.0  | 1501 | 2.7518          |
| 2.2313        | 20.0  | 1580 | 2.7373          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2